User identification by gesture recognition

ABSTRACT

A user can be identified and/or authenticated to an electronic device by analyzing aspects of a motion or gesture made by that user. At least one imaging element of the device can capture image information including the motion or gesture, and can determine time-dependent information about that motion or gesture in two or three dimensions of space. The time-dependent information can be used to identify varying speeds, motions, and other such aspects that are indicative of a particular user. The way in which a gesture or motion is made, in addition to the motion or gesture itself, can be used to authenticate an individual user. While other persons can learn the basic gesture or motion, the way in which each person makes that gesture or motion will generally be at least slightly different, which can be used to prevent unauthorized access to sensitive information, protected functionality, or other such content.

CROSS REFERENCE TO RELATED APPLICATION

This application is a Continuation of, and accordingly claims thebenefit of, U.S. patent application Ser. No. 13/172,727, filed with theU.S. Patent and Trademark Office on Jun. 29, 2011, assigned U.S. Pat.No. 8,693,726, which is hereby incorporated herein by reference.

BACKGROUND

People are increasingly interacting with computers and other electronicdevices in new and interesting ways. One such interaction approachinvolves making a detectable motion with respect to a device, which canbe detected using a camera or other such element. While simple motionscan be detected to provide input, there generally is no way to determinethe identity of the person making the gesture, unless there is anotherprocess being used in combination such as facial recognition, which canbe very resource intensive, particularly for mobile devices. If themotion is being made in contact with a display screen or other touchsensitive surface, a pattern such as a signature can be recognized toidentify the person. In many cases, however, a person can learn toapproximate another person's signature with enough accuracy to provideauthentication. Further, a user might not appreciate having tocontinually be in contact with the device in order to provide forauthentication of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an example environment in which various aspects canbe implemented in accordance with various embodiments;

FIGS. 2( a) and 2(b) illustrate an example motion that can be used as anidentifier in accordance with various embodiments;

FIGS. 3( a) and 3(b) illustrate an example motion and gesture that canbe used as an identifier in accordance with various embodiments;

FIG. 4 illustrates an example gesture that can be used as an identifierin accordance with various embodiments;

FIGS. 5( a), (b), (c), and (d) illustrate example images for analysiswith different types of illumination in accordance with variousembodiments;

FIG. 6 illustrates an example process for determining user identity thatcan be performed in accordance with various embodiments;

FIG. 7 illustrates an example computing device that can be used inaccordance with various embodiments;

FIG. 8 illustrates an example configuration of components of a computingdevice such as that illustrated in FIG. 7; and

FIG. 9 illustrates an example environment in which various embodimentscan be implemented.

DETAILED DESCRIPTION

Systems and methods in accordance with various embodiments of thepresent disclosure may overcome one or more of the aforementioned andother deficiencies experienced in conventional approaches to providinguser identification to an electronic device. In particular, variousembodiments enable a user to perform a specific motion or gestureassociated with that user, which can be analyzed by the electronicdevice (or a system, device, or service in communication with theelectronic device) to verify the identity of the person performing themotion or gesture. The electronic device can capture image informationincluding at least a portion of the user, and analyze that imageinformation to determine information about a motion or gesture, wherethat information can include position information for one or morefeatures of the user at one point in time and/or changes in the positioninformation over a period of time. The position information can becompared to position information stored for the user for use inidentifying that user, based upon the motion or gesture.

In various embodiments, a user can perform a signature or other specificmotion or gesture at a distance from an electronic device that can becaptured by at least one imaging element of the device. The capturedinformation can be analyzed to determine the location of at least onefeature, such as a user's fingertip, in the image information. Themotion of that feature over time then can be tracked, with a location ofthat feature being determined to correspond to a point in two- orthree-dimensional space. The location also can be stored with atimestamp or other such temporal information enabling speeds and/oraccelerations of a motion of gesture formation to be determined inaddition to the path of the motion or gesture itself. While uniquesignatures or motions can be difficult for another person to replicate,it can be especially difficult for a person to mimic the varying speedsand motions by which another person performs various parts of a motionor gesture formation. Such an approach can further be beneficial whenusing gestures or motions for user identification, as a user mightforget a complex gesture used to identify that user to a device, but ifthe gesture is forming the user's name or something otherwise easilyrecognizable in the air the user will likely remember the basic gesture.Further, motor memory is generally quite powerful, such that a user willtend to form a gesture such as the user's signature or initials in theair with similar speeds and motions even after a significant passage oftime.

In at least some embodiments, a user can utilize motions or gesturesthat utilize more than one point of reference. For example, a user mightmake a gesture with two or more fingers, with the motion of each ofthose fingers being currently tracked over time and compared to knownidentification information. Similarly, a user might use two hands, eyes,elbows, held object, or any of a number of other features that can betracked and analyzed for purposes of user identification.

In some embodiments, a user might not even need to make a motion to becaptured, but instead can utilize a specific “static” gesture. Forexample, a user might form a specific letter in sign language as anidentifier. In some embodiments, the motion the user uses to form thatgesture can be considered. In other embodiments, however, the analysismight instead include the relations of various feature points in thegesture. For example, different users will have different relativefinger lengths, palm widths, forearm lengths, and other such aspects,which can be combined with the gesture to help in determining aparticular person's identity.

A user can provide one or more motions or gestures over time to be usedin identifying that user. For example, a user might be identified to adevice through a password or signature validation or other such process.Once the user is identified, the user can perform a motion or gesturethat is to be associated with that user for use in subsequentidentification. One or more algorithms might analyze the motion orgesture and provide a “strength” or other such score or ratingindicating how likely it will be that a user cannot replicate thatmotion, such as may be based on variations in speed or acceleration,number of features that can be tracked, etc. The user then can performgestures or motions until the user is satisfied with the result (oranother criterion is met), and can periodically update the associatedmotion or gesture in order to provide added security.

Various lighting and capture approaches can be used in accordance withvarious embodiments. For example, ambient light or infrared imaging canbe used to determine the location of various features relative to thedevice. In some embodiments, a combination of ambient and infraredimaging can be used to remove background objects from the captured imageinformation in order to simplify, and improve the accuracy of imageprocessing. The information can be captured using any appropriate sensoror detector, such as a digital camera or infrared detector. Further, twoor more imaging elements can be used together in at least someembodiments to provide position information in three dimensions. Usingimage information as opposed to data from accelerometers or other typesof components can be further beneficial, as information such as velocityand position can often be determined with more accuracy using thecaptured image information.

Various other applications, processes and uses are presented below withrespect to the various embodiments.

FIG. 1 illustrates an example situation 100 wherein a user 102 wouldlike to provide gesture- and/or motion-based input to a computing device104, such as to provide an identity of that user to the device forpurposes of, for example, securely unlocking functionality on thedevice. Although a portable computing device (e.g., a smart phone, anelectronic book reader, or tablet computer) is shown, it should beunderstood that various other types of electronic device that arecapable of determining and processing input can be used in accordancewith various embodiments discussed herein. These devices can include,for example, notebook computers, personal data assistants, cellularphones, video gaming consoles or controllers, and portable mediaplayers, among others. In this example, the computing device 104 has atleast one image capture element 106 operable to perform functions suchas image and/or video capture. Each image capture element may be, forexample, a camera, a charge-coupled device (CCD), a motion detectionsensor, or an infrared sensor, or can utilize another appropriate imagecapturing technology.

In this example, the user 102 is performing a selected motion or gestureusing the user's hand 110. The motion can be one of a set of motions orgestures recognized by the device to correspond to a particular input oraction, or can be a specific motion or gesture associated with thatparticular user for identification purposes. If the motion is performedwithin a viewable area or angular range 108 of at least one of theimaging elements 106 on the device, the device can capture imageinformation including at least a portion of the motion or gesture,analyze the image information using at least one image analysis, featurerecognition, or other such algorithm, and determine movement of at leastone feature of the user between subsequent frames or portions of theimage information. This can be performed using any process known or usedfor determining motion, such as locating “unique” features in one ormore initial images and then tracking the locations of those features insubsequent images, whereby the movement of those features can becompared against a set of movements corresponding to the set of motionsor gestures, etc. Other approaches for determining motion- orgesture-based input can be found, for example, in co-pending U.S. patentapplication Ser. No. 12/332,049, filed Dec. 10, 2008, and entitled“Movement Recognition and Input Mechanism,” which is hereby incorporatedherein by reference.

In some embodiments, a user might select a motion that is to be used toidentify that user to an electronic device. For example, FIG. 2( a)illustrates an example situation 200 wherein a user authenticateshimself or herself to an electronic device by using an index finger to“write” the user's signature in the air in front of the device, within acapture range of at least one image capture element of the device. Theinformation captured by the image capture element can be analyzed todetermine a location of a specific feature in each frame or othersegment of information, in order to track the position of that featureover time. In this example, the feature being tracked is the user'sfingertip 202. The fingertip position can be determined, for example,through image analysis of a camera-captured image or intensity analysisof reflected IR radiation in a sensor-captured image. Various otherimaging approaches can be used as well. As illustrated, while the user'sfingertip 202 is forming the “signature” in the air, the captured imageinformation can be analyzed to determine a set of points along thesignature, each corresponding to a determined point of the user'sfingertip at a respective point in time, such as a time of capture of arespective frame of image information. An appropriate point to use inthe image information for the fingertip in a given image frame, forexample, can be determined using an appropriate method such as a localmaxima determination or centroid determination, etc.

The captured image information can be analyzed to determine a periodover which a detected motion might correspond to a gesture or other suchinput. In many embodiments, it may be too resource intensive to analyzeevery frame of captured video, unless the device is in a low frame rateor other such mode. In some embodiments, the device will periodicallyanalyze captured image information to attempt to determine if a featurein the image information appears to indicate a user making a motion orgesture. In at least some embodiments, this can cause the device tobegin to capture information with a higher frame rate or frequency,during which time a gesture or input analysis algorithm can be used toanalyze the information. In other embodiments, the device might utilizea rolling buffer of image information, keeping image information from arecent period, such as the last ten seconds. When a possible gesture oruser motion is detected, the device might also analyze the informationin the buffer in case the device missed the beginning of a motion orgesture at the time of motion detection. Various other approaches can beused as well as should be apparent in light of the teachings andsuggestions contained herein.

FIG. 2( b) illustrates an example set of points 210 that can be capturedfor a motion such as that illustrated in FIG. 2( a). In at least someembodiments, these points are captured at relatively equidistant pointsin time. In some embodiments, such as where there is a single camera,the points might be determined in two dimensions (x, y). If depthinformation is capable of being determined, such as where there are twoor more image capture elements doing triangulation or stereoscopicimaging, for example, the points might instead be determined in threedimensions (x, y, z) in space. The collection of points for a givenmotion or gesture then can be compared against sets of points stored ina library or other such data repository, where each of those setscorresponds to a particular user, motion, gesture, or other such aspect.Using one or more point-matching algorithms, the determined collectionof points can be compared against at least a portion of the stored setsuntil a set of points matches with a minimum level of certainty orconfidence, etc. (or until there are no more sets of points to attemptto match). In some embodiments, a curve or continuous line or functioncan be fit to the collection of points and compared against a set ofcurves, for example, which can help improve the matching process inembodiments where the points are relatively far apart and the timing ofthose points can potentially otherwise affect the matching process.

In at least some embodiments, the process can further take advantage ofthe fact that the device can provide timing (absolute or relative)information for each point or between each pair of points. Thus, eachpoint can have an additional dimension (x, y, t) or (x, y, z, t) thatcan including timing information in addition to positional information.As mentioned above, one person might learn how to trace out thesignature of another person with a reasonable degree of accuracy. Itwill be much harder, however, for a person to also learn the varyingspeed and/or motion with which another person forms that signature (orother motion, gesture, etc.) Thus, having timing information in additionto position information can help to more accurately identify the personmaking the motion or gesture.

The sets of points can further be encoded according to any appropriatestandard or framework. In some embodiments, each tracked or monitoredpoint or feature of a user or other object can correspond to a stream ofrelatively continuous points. For multiple points (i.e., when trackingall five fingers of a user's hand) there can be multiple encodedstreams. Each stream can be stored as a sequence of points for matchingagainst one or more known sequences of points. In at least someembodiments, each point has a timestamp enabling speed, acceleration, orother such information to be determined. For a given feature, such as auser's hand, there might be ten features (e.g., brightest or closestpoints, identified feature points, etc.) that are monitored at anappropriate sample rate, such as between 100 Hz and 1 kHz, or at around120 Hz for at least one embodiment. Such an approach might result inaround one thousand points for a second-long period of time, which canprovide a desired level of accuracy for identification while avoidingthe processing of potentially millions of points if trying to doconventional image-based tracking. In some embodiments, an algorithmmight attempt to further reduce the number of points to be trackedand/or analyzed, such as when a given feature does not movesubstantially between capture times, etc.

In FIG. 2( b) the points can be analyzed to determine that the usermaking the gesture moves the fastest during a portion of forming thecursive “j” as indicated by separation 216. The user might move theslowest around a turn-around point, such as near a portion 212 of thecursive “a” where the points are significantly close together. Theportion of the signature near the end 218 for this user might be at aspeed roughly in-between. By looking at the relative distances betweenadjacent points in both position and time, a set of speeds and/oraccelerations (or relatively continuous speed function, etc.) can bedetermined for the signature. This speed-related information then canalso be compared against stored information for one or more users, andused to find a more accurate match than for position or trajectoryalone. For example, two users might be able to sign the name “Jan” withsufficient similarity, but the motion and speeds they user to form thatname will typically be significantly different, providing a moreaccurate identification result when receiving the signature from one ofthose users. In some embodiments, a set of speeds between each point canbe determined for matching, while in other embodiments speeds and/oraccelerations can be determined for specific points or regions of thegesture, as may correspond to areas of highest and lowest speed, etc.

In at least some embodiments, a device might track more than one pointor feature over time. For example, FIG. 3( a) illustrates an examplesituation 300 wherein a user makes a gesture that involves all fivefingers, here going from an open hand to a particular configuration ofthe user's fingers. If the location of each fingertip is able todetermined from the captured image information, the relative motion ofeach fingertip can be tracked in position and/or time. The motion ofeach fingertip can form a path 302, which can be analyzed using anapproach such as those described above with respect to a single finger.In addition, however, the paths can also be compared with each other toprovide additional information. For example, each user may have fingersof different length and hands of different size and shape, and might useslightly different separations during the motion and/or at the ends ofthe motion. Thus, in addition to getting five times the information fromthe live separate paths, the information can also be used to determinerelative speeds and/or positions between the different features.

For example, FIG. 3( b) illustrates an example set of points 310 thatcould be determined for the motion of FIG. 3( a). In this example, itcan be seen that the path of travel for each finger can be different, aswell as the speed of each path. Further, these paths can have a distinctorientation with respect to each other. For example, when thisparticular user makes the gesture, the paths 312 and 314 of twodifferent fingers cross by a certain amount. For another user, the pathsmight cross by a different amount, or may not cross at all. Thus, therelative motions of multiple features can be yet another indicator ofthe identity of a person, as the way a user makes multiple motions canbe compared against each other as well.

Further still, the image information can be analyzed to determine one ormore physical characteristics of the user. For example, FIG. 4illustrates an example image 400 captured showing a gesture being formedby the hand of a user 402. The user might have formed this gesture usinga particular motion, as discussed above, or might simply want to usethis particular configuration as an identifier. As discussed above, theimage information can be analyzed to determine the location of specificfeatures in the gesture, such as the distance 404 between adjacentfingertips, etc. In addition, however, various other factors can beanalyzed as well, such as the relative lengths of each finger, palmdimensions, relative separations of fingers while making the gesture,and any of a number of other such aspects that might be unique for, andthus indicative of, a particular user. In this way, even if anotherperson learns the user's identifying gesture, unless that other personhas physical features substantially similar to those of the user, theperson will be unable to fake a device into identifying that person asthe user of the device, etc. If available, other information can be usedas well, such as the relative length of the user's forearm, thickness ofthe user's wrist, or other such information.

As mentioned, various types of information can be used to attempt tolocate and track specific features over time. One approach utilizesambient-light imaging with a digital camera (still or video) to captureimages that can be analyzed with an image recognition algorithm. As isknown in the art, and as illustrated in the example image 500 of FIG. 5(a), however, ambient light images can include information for a numberof different objects and thus can be very processor and time intensiveto analyze. For example, an image analysis algorithm would not only haveto differentiate the hand from the door and sidewalk in the image, butwould also have to identify the hand as a hand, regardless of the hand'sorientation. Such an approach can require shape or contour matching, forexample, which can still be relatively processor intensive. A lessprocessor intensive approach would be to separate the hand from thebackground before analysis.

In at least some embodiments, a light emitting diode (LED) or othersource of illumination can be triggered to produce illumination over ashort period of time in which an image capture element is going to becapturing image information. With a sufficiently fast capture or shutterspeed, for example, the LED can illuminate a feature relatively close tothe device much more than other elements further away, such that abackground portion of the image can be substantially dark (or otherwise,depending on the implementation). For example, FIG. 5( b) illustrates anexample image 510 wherein an LED or other source of illumination isactivated (e.g., flashed or strobed) during a time of image capture ofat least one gesture sensor. As can be seen, since the user's hand isrelatively close to the device the hand will appear relatively bright inthe image. Accordingly, the background images will appear relatively, ifnot almost entirely, dark. This approach can be particularly beneficialfor infrared (IR) imaging in at least some embodiments. Such an imagecan be much easier to analyze, as the hand has been effectivelyseparated out from the background, and thus can be easier to trackthrough the various images. Further, there is a smaller portion of theimage to analyze to attempt to determine relevant features for tracking.In embodiments where the detection time is short, there will berelatively little power drained by flashing the LED in at least someembodiments, even though the LED itself might be relatively power hungryper unit time.

Such an approach can work both in bright or dark conditions. A lightsensor can be used in at least some embodiments to determine whenillumination is needed due at least in part to lighting concerns. Inother embodiments, a device might look at factors such as the amount oftime needed to process images under current conditions to determine whento pulse or strobe the LED. In still other embodiments, the device mightutilize the pulsed lighting when there is at least a minimum amount ofcharge remaining on the battery, after which the LED might not fireunless directed by the user or an application, etc. In some embodiments,the amount of power needed to illuminate and capture information usingthe gesture sensor with a short detection time can he less than theamount of power needed to capture an ambient light image with a rollingshutter camera without illumination.

In embodiments where there is not a sufficiently fast shutter, wherethere is a rolling shutter effect, or in other such situations, it mightbe difficult to substantially prevent detecting reflections from otherobjects near the device. For example, FIG. 5( c) illustrates an exampleimage 520 that could be captured using an infrared (IR) sensor, forexample, wherein the hand is easier to locate in the image but thebackground is still present enough that an image processing algorithmmight have to process other objects in the image, or might not be ableto quickly locate a specific feature with a minimum level of certainty.In at least some embodiments, a device can capture both an ambient lightimage, such as in FIG. 5( a), and a reflected IR image, such as in FIG.5( h). By having both images, one or more algorithms can be used toshift the images (to account for distance offset of the imagingelements) and then subtract the ambient light image 500 from thereflected IR image 520. The resulting image would be substantiallydominated by the hand of the user. In at least some embodiments, aweighted subtraction can be performed when it is determined (due tocontrast, lighting, or other such aspects) that the backgroundinformation is likely much more intense in the ambient light image thanthe IR image, and vice versa. In some cases, a set of weightedcomparisons can be performed until one or more features can be locatedwith a minimum level of confidence.

In at least some embodiments, the intensity of the reflected IR can beused to determine one or more features to be tracked between images. Forexample, in the example IR image 530 of FIG. 5( d) the user is using asingle finger to perform a motion as input to the device. In such anexample, the tip of the user's finger typically will be the closestportion of the user's hand to the device. Thus, if the relativebrightness can be determined with an acceptable level ofdifferentiation, the tip of the user's finger can be determined at leastin part by looking for the brightest region in the IR image. Otherfeatures such as the tip of the user's thumb or parts of other fingersmight appear relatively bright as well, which can help to determineadditional points to track that can be further indicative of an identityof the user.

FIG. 6 illustrates an example process 600 for enabling gestureidentification for such a computing device that can be used inaccordance with various embodiments. It should be understood that, forany process discussed herein, there can be additional, fewer, oralternative steps performed in similar or alternative orders, or inparallel, within the scope of the various embodiments unless otherwisestated. In this example, gesture detection is activated on a computingdevice 602. In at least some embodiments this is activated manually bythe user or upon activation of an application, for example, but can alsobe continually active in at least a low power state in otherembodiments.

In this example, a computing device might run in a low power or lowresolution mode, such as where there has not been recent gesture input.Such a mode can comprise, for example, capturing image information at alower capture rate or analyzing captured information less frequently. Inat least some embodiments, the device will change into a different modeor otherwise activate certain functionality once motion is detected 604,whether through image analysis, use of a motion detector, etc. In thisexample, a light detector or other such mechanism (hardware and/orsoftware analyzing the captured image information) can determine whetherthere is sufficient lighting 606. If it is determined that the light isnot sufficient 608, or if light is otherwise needed (such as for IRillumination), one or more illumination sources can be activated 612before subsequent image information is captured 610. As mentionedelsewhere herein, the illumination source can be any appropriate sourceoperable to provide an adequate amount and/or type of illumination(e.g., white light or IR), at any appropriate time (e.g., continuouslyduring image capture or strobed with a timing of the capture).

The captured image information, which can include cached or other suchtemporarily stored image information as discussed above, can be analyzedto attempt to determine one or more gesture points 612. As discussed,this can include user features recognized in a string of images, brightregions in IR image information, points of a certain proximity ordistance in the image information, etc. One or more algorithms can notonly attempt to locate such points or features, but also determinecoordinates or other appropriate values and track values for thosepoints between successive frames or other portions of the captured imageinformation. As discussed, this can include capturing information forone or more points from an image capture stream and storing values forthose points as a potential gesture set.

If the analyzed information is indicative of a potential gesture and aset of potential gesture points can be obtained, this potential gestureset can be compared against one or more gesture patterns in a gesturelibrary 614 or other such source. Any appropriate matching algorithm canbe used as discussed or suggested herein, or as is known or used in theart for attempting to match point sets, functions, paths, or other suchfeatures. If no match can be found with at least a minimum confidence,level of certainty, or other such criterion or threshold 618, thegesture point information (and associated image information) can bediscarded 620. If a match can be determined with an appropriateconfidence, etc., input corresponding to that gesture can be accepted622. In at least some embodiments, this can correspond to acceptingidentifying information for a particular user, based upon recognition ofa type of motion or gesture known or determined for that user. Variousother approaches can be used as well as discussed or suggested elsewhereherein.

FIG. 7 illustrates an example computing device 700 that can be used toperform such a method in accordance with various embodiments. In thisexample, the device has a conventional digital camera 704 on a same sideof the device as a display element 702, enabling the device to captureimage information about a user of the device during typical operationwhere the user is at least partially in front of the display element. Inat least some embodiments, the display element 702 can he a capacitivescreen or other such element that is able to determine position of afeature of a user within a given distance (e.g., 3-5 cm) of the screenusing capacitive measurements, and track a position of that feature overtime. Information from the capacitive screen can be used instead of theimage information, or in addition to the image information in order toimprove accuracy and/or fill in motions where a feature of the usermight be too close to the device to be adequately imaged by an imagingelement, etc. In addition, the example computing device includes aninfrared (IR) sensor 706 (or transceiver, etc.) positioned on the sameside of the device that can be used to determine gesture input from theuser when at relatively the same location. Such a configuration isuseful when ambient light image information is subtracted from IR imageinformation, but it should be understood that there can be additional orfewer cameras, sensors, or other such elements on the same or othersides or locations of the device as well within the scope of the variousembodiments, such as may enable gesture or image input from any desireddirection or location with respect to the device.

In this example, a light sensor 708 is included that can be used todetermine an amount of light in a general direction of an image to becaptured and at least one illumination element 710, such as a whitelight emitting diode (LED) or infrared (IR) emitter, as discussedelsewhere herein, for providing illumination in a particular range ofdirections when, for example, there is insufficient ambient lightdetermined by the light sensor or reflected IR radiation is to becaptured. Various other elements and combinations of elements can beused as well within the scope of the various embodiments as should beapparent in light of the teachings and suggestions contained herein.

In order to provide various functionality described herein, FIG. 8illustrates an example set of basic components of a computing device800, such as the device 700 described with respect to FIG. 7. In thisexample, the device includes at least one central processor 802 forexecuting instructions that can be stored in at least one memory deviceor element 804. As would be apparent to one of ordinary skill in theart, the device can include many types of memory, data storage orcomputer-readable storage media, such as a first data storage forprogram instructions for execution by the processor 802, the same orseparate storage can be used for images or data, a removable storagememory can be available for sharing information with other devices, etc.The device typically will include some type of display element 806, suchas a touch screen, electronic ink (e-ink), organic light emitting diode(OLED) or liquid crystal display (LCD), although devices such asportable media players might convey information via other means, such asthrough audio speakers. In at least some embodiments, the display screenprovides for touch or swipe-based input using, for example, capacitiveor resistive touch technology.

As discussed, the device in many embodiments will include at least oneimage capture element 808, such as one or more cameras that are able toimage a user, people, or objects in the vicinity of the device. An imagecapture element can include, or be based at least in part upon anyappropriate technology, such as a CCD or CMOS image capture elementhaving a determined resolution, focal range, viewable area, and capturerate. The device can also include at least one separate gesturecomponent 810, such as an IR sensor or detector, operable to captureinformation for use in determining gestures or motions of the user,which will enable the user to provide input through the portable devicewithout having to actually contact and/or move the portable device. Thedevice also can include at least one illumination element 812, as mayinclude one or more light sources (e.g., white light LEDs, IR emitters,or flashlamps) for providing illumination and/or one or more lightsensors or detectors for detecting ambient light or intensity, etc.

The example device can include at least one additional input device ableto receive conventional input from a user. This conventional input caninclude, for example, a push button, touch pad, touch screen, wheel,joystick, keyboard, mouse, trackball, keypad or any other such device orelement whereby a user can input a command to the device. These I/Odevices could even be connected by a wireless infrared or Bluetooth orother link as well in some embodiments. In some embodiments, however,such a device might not include any buttons at all and might becontrolled only through a combination of visual (e.g., gesture) andaudio (e.g., spoken) commands such that a user can control the devicewithout having to be in contact with the device.

As discussed, different approaches can be implemented in variousenvironments in accordance with the described embodiments. For example,FIG. 9 illustrates an example of an environment 900 for implementingaspects in accordance with various embodiments. As will be appreciated,although a Web-based environment is used for purposes of explanation,different environments may be used, as appropriate, to implement variousembodiments. The system includes an electronic client device 902, whichcan include any appropriate device operable to send and receiverequests, messages or information over an appropriate network 904 andconvey information back to a user of the device. Examples of such clientdevices include personal computers, cell phones, handheld messagingdevices, laptop computers, set-top boxes, personal data assistants,electronic book readers and the like. The network can include anyappropriate network, including an intranet, the Internet, a cellularnetwork, a local area network or any other such network or combinationthereof. Components used for such a system can depend at least in partupon the type of network and/or environment selected. Protocols andcomponents for communicating via such a network are well known and willnot be discussed herein in detail. Communication over the network can beenabled via wired or wireless connections and combinations thereof. Inthis example, the network includes the Internet, as the environmentincludes a Web server 906 for receiving requests and serving content inresponse thereto, although for other networks, an alternative deviceserving a similar purpose could be used, as would be apparent to one ofordinary skill in the art.

The illustrative environment includes at least one application server908 and a data store 910. It should be understood that there can beseveral application servers, layers or other elements, processes orcomponents, which may be chained or otherwise configured, which caninteract to perform tasks such as obtaining data from an appropriatedata store. As used herein, the term “data store” refers to any deviceor combination of devices capable of storing, accessing and retrievingdata, which may include any combination and number of data servers,databases, data storage devices and data storage media, in any standard,distributed or clustered environment. The application server 908 caninclude any appropriate hardware and software for integrating with thedata store 910 as needed to execute aspects of one or more applicationsfor the client device and handling a majority of the data access andbusiness logic for an application. The application server providesaccess control services in cooperation with the data store and is ableto generate content such as text, graphics, audio and/or video to betransferred to the user, which may be served to the user by the Webserver 906 in the form of HTML, XML or another appropriate structuredlanguage in this example. The handling of all requests and responses, aswell as the delivery of content between the client device 902 and theapplication server 908, can be handled by the Web server 906. It shouldbe understood that the Web and application servers are not required andare merely example components, as structured code discussed herein canbe executed on any appropriate device or host machine as discussedelsewhere herein.

The data store 910 can include several separate data tables, databasesor other data storage mechanisms and media for storing data relating toa particular aspect. For example, the data store illustrated includesmechanisms for storing content (e.g., production data) 912 and userinformation 916, which can be used to serve content for the productionside. The data store is also shown to include a mechanism for storinglog or session data 914. It should be understood that there can be manyother aspects that may need to be stored in the data store, such as pageimage information and access rights information, which can be stored inany of the above listed mechanisms as appropriate or in additionalmechanisms in the data store 910. The data store 910 is operable,through logic associated therewith, to receive instructions from theapplication server 908 and obtain, update or otherwise process data inresponse thereto. In one example, a user might submit a search requestfor a certain type of item. In this case, the data store might accessthe user information to verify the identity of the user and can accessthe catalog detail information to obtain information about items of thattype. The information can then be returned to the user, such as in aresults listing on a Web page that the user is able to view via abrowser on the user device 902. Information for a particular item ofinterest can be viewed in a dedicated page or window of the browser.

Each server typically will include an operating system that providesexecutable program instructions for the general administration andoperation of that server and typically will include computer-readablemedium storing instructions that, when executed by a processor of theserver, allow the server to perform its intended functions. Suitableimplementations for the operating system and general functionality ofthe servers are known or commercially available and are readilyimplemented by persons having ordinary skill in the art, particularly inlight of the disclosure herein.

The environment in one embodiment is a distributed computing environmentutilizing several computer systems and components that areinterconnected via communication links, using one or more computernetworks or direct connections. However, it will be appreciated by thoseof ordinary skill in the art that such a system could operate equallywell in a system having fewer or a greater number of components than areillustrated in FIG. 9. Thus, the depiction of the system 900 in FIG. 9should be taken as being illustrative in nature and not limiting to thescope of the disclosure.

The various embodiments can be further implemented in a wide variety ofoperating environments, which in some cases can include one or more usercomputers or computing devices which can be used to operate any of anumber of applications. User or client devices can include any of anumber of general purpose personal computers, such as desktop or laptopcomputers running a standard operating system, as well as cellular,wireless and handheld devices running mobile software and capable ofsupporting a number of networking and messaging protocols. Such a systemcan also include a number of workstations running any of a variety ofcommercially-available operating systems and other known applicationsfor purposes such as development and database management. These devicescan also include other electronic devices, such as dummy terminals,thin-clients, gaming systems and other devices capable of communicatingvia a network.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially-available protocols, such as TCP/IP, OSI, FTP,UPnP, NFS, CIFS and AppleTalk. The network can be, for example, a localarea network, a wide-area network, a virtual private network, theInternet, an intranet, an extranet, a public switched telephone network,an infrared network, a wireless network and any combination thereof.

In embodiments utilizing a Web server, the Web server can run any of avariety of server or mid-tier applications, including HTTP servers, FTPservers, CGI servers, data servers, Java servers and businessapplication servers. The server(s) may also be capable of executingprograms or scripts in response requests from user devices, such as byexecuting one or more Web applications that may be implemented as one ormore scripts or programs written in any programming language, such asJava®, C, C# or C++ or any scripting language, such as Perl, Python orTCL, as well as combinations thereof. The server(s) may also includedatabase servers, including without limitation those commerciallyavailable from Oracle®, Microsoft®, Sybase® and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (SAN) familiar to those skilled inthe art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (CPU), at least one inputdevice (e.g., a mouse, keyboard, controller, touch-sensitive displayelement or keypad) and at least one output device (e.g., a displaydevice, printer or speaker). Such a system may also include one or morestorage devices, such as disk drives, optical storage devices andsolid-state storage devices such as random access memory (RAM) orread-only memory (ROM), as well as removable media devices, memorycards, flash cards, etc.

Such devices can also include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium representing remote, local, fixed and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs such as a client applicationor Web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disk (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices or any other medium which canhe used to store the desired information and which can be accessed by asystem device. Based on the disclosure and teachings provided herein, aperson of ordinary skill in the art will appreciate other ways and/ormethods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

What is claimed is:
 1. A computing device, comprising: a processor; acamera; and memory including instructions that, upon being executed bythe processor, cause the computing device to: capture, using the camera,a plurality of images each including a respective representation of atleast one object corresponding to a user; identify a respective positionof the respective representation of the at least one object in eachimage of the plurality of images compare the respective position of therespective representation of the at least one object in each image and arespective time for each respective position to one or moreuser-specific measurements corresponding to a manner of the userperforming a gesture, the one or more user-specific measurementsincluding at least one of: (a) a path of movement of the gesture, (b)one or more of relative timing, acceleration, or velocity informationcorresponding to the gesture, or (c) measurements of at least onefeature of the user that is used to perform the gesture; and identifythe user in response to determining that the respective position of therespective representation of the at least one object identified in eachimage and the respective time for each respective position matches theone or more user-specific measurements with at least a minimum level ofconfidence.
 2. The computing device of claim 1, wherein theinstructions, upon being executed, further cause the computing deviceto: restrict access to at least some content or functionality of thecomputing device in response to determining that the respective positionof the respective representation of the at least one object in eachimage and the respective time for each respective position do not matchthe one or more user-specific measurements with the at least the minimumlevel of confidence.
 3. The computing device of claim 1, furthercomprising: an infrared emitter; and an infrared detector, wherein theinstructions, upon being executed, further cause the computing deviceto: activate the infrared emitter during a period of time correspondingto when the plurality of images are captured; capture a second pluralityof images using the infrared detector during the period of time; andsubtract a weighted amount of respective ambient light image informationin each image of the plurality of images and respective reflectedinfrared image information in each corresponding image of the secondplurality of images to substantially remove background information ineach corresponding image of the second plurality of images.
 4. Acomputer-implemented method of identifying a user, comprising: undercontrol of one or more computing devices including executableinstructions, obtaining a plurality of images each including arespective representation of at least one object corresponding to theuser; identifying a respective set of one or more points correspondingto the respective representation of the at least one object in eachimage of the plurality of images comparing the respective set of pointsidentified in each image and a respective time for each respective setof points to one or more measurements corresponding to a manner of theuser performing a gesture; and identifying the user in response todetermining that the respective set of points identified in each imageand the respective time for each respective set of points matches theone or more measurements with at least a minimum level of confidence. 5.The computer-implemented method of claim 4, wherein the one or moremeasurements include at least one of: (a) a path of movement of thegesture, (b) one or more of relative timing, acceleration, or velocityinformation corresponding to the gesture, or (c) measurements of atleast one feature of the user that is used to perform the gesture. 6.The computer-implemented method of claim 4, wherein identifying therespective set of points in each image is based at least in part uponone of image recognition, proximity detection, or intensity analysis. 7.The computer-implemented method of claim 4, wherein the respective setof points corresponding to the respective representation of the at leastone object in each image are three-dimensional points.
 8. Thecomputer-implemented method of claim 4, wherein each image of theplurality of images includes respective ambient light image information,and the method further comprises: obtaining a second plurality of imagesduring a period of time corresponding to when the plurality of imagesare obtained, each image of the second plurality of images includingrespective reflected infrared image information.
 9. Thecomputer-implemented method of claim 8, further comprising: subtractinga weighted amount of the respective ambient light image information ineach image of the plurality of images and the respective reflectedinfrared image information in each corresponding image of the secondplurality of images to substantially remove background information ineach corresponding image of the second plurality of images.
 10. Thecomputer-implemented method of claim 4, wherein obtaining the pluralityof images includes capturing the plurality of images using a camera of acomputing device in response to detecting motion within a field of viewof the camera.
 11. The computer-implemented method of claim 4, furthercomprising: prompting the user to perform a second gesture; capturing asecond plurality of images and a respective second time corresponding toeach image of the second plurality of images, the second plurality ofimages each including a respective representation of at least one secondobject corresponding to the user; and providing to the user anindication of a degree of difficulty of replicating the second gesture.12. The computer-implemented method of claim 4, further comprising:causing at least one source of illumination to be activated at leastduring a period of time corresponding to when a first image of theplurality of images is obtained and a second image of the plurality ofimages is obtained.
 13. The computer-implemented method of claim 12,wherein the at least one source of illumination includes at least one ofa white light or an infrared source of illumination, the at least one ofthe white light or the infrared source of illumination being activatedcontinuously or periodically during the period of time.
 14. Thecomputer-implemented method of claim 4, further comprising: storing atleast one portion of the plurality of images in a rolling buffer,wherein identifying the respective set of points corresponding to therespective representation of the at least one object in each imageoccurs after a fixed period of time of storing the at least one portionof the plurality of images in the rolling buffer.
 15. Thecomputer-implemented method of claim 4, further comprising: fitting acurve or a function to the respective set of points corresponding to therespective representation of the at least one object in each image. 16.The computer-implemented method of claim 4, further comprising:restricting access to at least some content or functionality of thecomputing device in response to determining that the respective set ofpoints identified in each image and the respective time for eachrespective set of points do not match the one or more measurements withthe at least the minimum level of confidence.
 17. Thecomputer-implemented method of claim 4, further comprising: deactivatinga gesture input mode if no gesture is detected within a specified periodof inactivity.
 18. A non-transitory computer-readable storage mediumstoring instructions for identifying a user that, upon being executed bya processor, cause the processor to: obtain a plurality of images eachincluding a respective representation of at least one objectcorresponding to the user; identify a respective set of one or morepoints corresponding to the respective representation of the at leastone object in each image of the plurality of images compare therespective set of points identified in each image and a respective timefor each respective set of points to one or more measurementscorresponding to a manner of the user performing a gesture, the one ormore measurements including at least one of: (a) a path of movement ofthe gesture, (b) one or more of relative timing, acceleration, orvelocity information corresponding to the gesture, or (c) measurementsof at least one feature of the user that is used to perform the gesture;and identify the user in response to determining that the respective setof points identified in each image and the respective time for eachrespective set of points matches the one or more measurements with atleast a minimum level of confidence.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein the instructions,upon being executed, further cause the processor to: prompt the user toperform a second gesture; capture a second plurality of images and arespective second time corresponding to each image of the secondplurality of images, the second plurality of images each including arespective representation of at least one second object corresponding tothe user; and provide to the user an indication of a degree ofdifficulty of replicating the second gesture.
 20. The non-transitorycomputer-readable storage medium of claim 18, wherein the instructions,upon being executed, further cause the processor to: store at least oneportion of the plurality of images in a rolling buffer, wherein theinstructions, upon being executed to cause the processor to identify therespective set of points corresponding to the respective representationof the at least one object in each image, are executed after a fixedperiod of time of storing the at least one portion of the plurality ofimages in the rolling buffer.